an efficient threshold based power management mechanism for heterogeneous soft real time clusters n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters PowerPoint Presentation
Download Presentation
An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters

Loading in 2 Seconds...

play fullscreen
1 / 29

An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters - PowerPoint PPT Presentation


  • 95 Views
  • Uploaded on

An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters. Leping Wang, Ying Lu University of Nebraska-Lincoln, USA September 4, 2014. Outline. Motivation Related Work Problem Statement Threshold-based approach Evaluation Conclusion. Motivation.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters' - azure


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
an efficient threshold based power management mechanism for heterogeneous soft real time clusters

An Efficient Threshold-Based Power ManagementMechanism for HeterogeneousSoft Real-Time Clusters

Leping Wang, Ying Lu

University of Nebraska-Lincoln, USA

September 4, 2014

outline
Outline
  • Motivation
  • Related Work
  • Problem Statement
  • Threshold-based approach
  • Evaluation
  • Conclusion
motivation
Motivation
  • Why power management (PM) for heterogeneous clusters
    • The power-related costs dominate the total cost of ownership of a cluster system
    • Most PM mechanisms are applicable to homogenous systems
    • Heterogeneous clusters are already widespread
motivation1
Motivation
  • Opportunities for PM in heterogeneous clusters
    • Turn off or hibernate idle servers
    • Dynamically scale operating frequency/voltage (DVS) for underutilized servers
    • Distribute more requests to power-efficient servers
new challenges
New Challenges
  • Decide not only how many but also which cluster servers should be used to process current requests, when necessary
  • Identifying the optimal load distribution for a heterogeneous cluster is a non-trivial task
related work
Related Work
  • PM in homogeneous systems
    • [Bianchini et al. 2004], [Bohrer et al. 2002],

[Chase et al. 2001], [Chen et al. 2005], [Elnozahy et al. 2002],

[Rajamani et al. 2003]

  • PM in heterogeneous systems
    • [Heath et al. PPoPP2005], [Rusu et al. RTAS2006]
related work1
Related Work
  • Current PM approaches for heterogeneous clusters
    • Search-based algorithms
    • Extensive performance measurements
    • Long optimization process
  •  high customization costs upon new installations, server failures, cluster upgrades or other changes
goal and components
Goal and Components
  • Goal
    • Near-optimal power consumption
    • QoS (average response time guarantee)
    • Efficient algorithm
  • Three components
    • Vary-on/off
    • DVS with feedback control
    • Optimal workload distribution
system model
System Model

1.CPU-bounded server clusters (e.g. web server cluster)

2.One front-end server

3.N heterogeneous back- end servers

optimization problem
Optimization Problem

Cast the PM to an optimization problem

  • Objective: Minimize the total cluster power consumption J
  • QoS constraints:
  • Decisions on
    • Which servers should be used to process the current workload cluster , i.e., decide xi: 0 or 1
    • How should the workload clusterbe distributed to active back-end servers, i.e., decide λi such that
    • According to i, back-end server set its CPU frequency fi
power and capacity models
Power and Capacity Models

: The ith server’s throughput

: The ith server’s performance coefficient

  • Power Model
  • Capacity Model

: Total power consumption

: The ith server’s on/off state

: The ith server’s constant power consumption

: The ith server’s operating frequency

: The ith server’s dynamic power consumption

optimization problem1
Optimization Problem
  • According to the M/M/1 queuing model and our server capacity model, we have
  • To make , we know
optimization problem2
Optimization Problem
  • The optimization problem is formed as follows
    • Minimize:

Subject to:

optimization problem3
Optimization Problem
  • No analytical method to get the closed-form solution on i and xi
  • Time complexity of search-based algorithm
  • Basic idea of our efficient PM
    • Use a heuristic method to decouple decisions on xiand i, then solve them separately to obtain near-optimal solutions.
threshold based approach
Threshold-Based Approach
  • An efficient PM heuristic
    • Efficient offline analysis:
      • Divides the possible workload range into N sub-ranges
      • For each sub-range, the PM decisions are derived offline
    • Online execution:

Periodically,

      • Front-end server: workload clusteris predicted and depending on the range cluster falls into, the corresponding PM decisions will be followed
      • Back-end server: applies DVS mechanism to decide fi
offline analysis
Offline Analysis
  • Order the heterogeneous back-end servers, i.e., generates a sequence, called ordered server list
  • Produce server activation thresholds 1, 2, … N such that if cluster  (k-1, k], it is optimal to turn on the first k servers of the ordered server list
  • Optimal workload distribution problem is solved for the N scenarios where cluster  (k-1, k], k=1, 2, …, N (time complexity: (N))
offline analysis1
Offline Analysis
  • When cluster  (k-1, k], the first k servers of the ordered server list are turned on and the optimization problem becomes
    • Minimize:

Subject to:

Solution: the optimal workload distribution i

algorithm
Algorithm
  • Our method, denoted as TP-CP-OP
    • Server Ordered ListOrder all back-end servers according to their Typical Power (TP) efficiencies
    • Server Activation Thresholds Consider both server Capacity constraints and Power efficiencies (CP)
    • Optimal Workload Distribution (OP)
dynamic voltage scaling
Dynamic Voltage Scaling

Feedforward M/M/1 Based Controller

i

fi

errori

Feedback PI Controller

fi

ith Back-end Server

+

+

-

Ri

evaluation
Evaluation
  • A small cluster with 4 back-end servers
    • Continuous operating frequency ranged in (0, fi_max]
    • Discrete operating frequency levels in [fi_min , fi_max]
  • A large cluster with 128 back-end servers in 8 different types
evaluation1
Evaluation
  • Synthetic workload and Real Workload
  • Desired average response time is set at 1s
  • Evaluation metrics: average response time and power consumption
  • Each simulation lasts 3000s
  • Power management in every 30s
evaluation2
Evaluation
  • Baseline algorithms
    • Threshold-based approaches: AA−AA−CA, SP−CA−CA, EE-RT-HSC
    • Optimal power management solution OPT-SOLN obtained by a search-based algorithm
evaluation3
Evaluation
  • Average Response Time
evaluation4
Evaluation
  • Power Consumption
conclusion
Conclusion
  • A efficient power management algorithm for heterogeneous server clusters
    • Mathematical models based
      • Minimum performance profiling
    • Workload threshold based
      • Low algorithm time complexity
    • Balance overhead and optimal solution
      • Fewer number of server on/off changes
      • Near-optimal power consumption
technical report
TechnicalReport
  • L. Wang and Y. Lu. Efficient power management of heterogeneous soft real-time clusters. Technical Report TR-UNL-CSE-2008-0004, University of Nebraska-Lincoln, 2008
leping wang ying lu

Questions

or

Comments?

?

Thanks!

Leping Wang, Ying Lu

evaluation5
Evaluation

Effect of Feedback Control

evaluation6
Evaluation

Effect of Feedback Control